Direct actions of nicotine in the CNS appear to be essential for its reinforcing properties. However, activation of nicotinic acetylcholine receptors (nAChRs) on afferent sensory nerve fibers are important components of addiction to, and withdrawal from, cigarette smoking. The present study was to identify the neuroanatomical substrates activated by the peripheral actions of nicotine and to determine whether these sites overlap brain structures stimulated by direct actions of nicotine. Mouse brains were examined by immunohistochemistry for c-Fos protein after intraperitoneal injection of either nicotine (NIC, 30 and 40 µg/kg) and/or nicotine pyrrolidine methiodide (NIC-PM, 20 and 30 µg/kg). NIC-PM induced c-Fos immunoreactivity (IR) at multiple brain sites. In the brainstem, c-Fos IR was detected in locus coeruleus, laterodorsal tegmental nucleus and pedunculotegmental nucleus. In the midbrain, c-Fos IR was observed in areas overlapping the ventral tegmental area (VTA) which includes paranigral nucleus, parainterfascicular nucleus, parabrachial pigmental area and rostral VTA. Other structures of the nicotine brain-reward circuitry activated by NIC-PM included hypothalamus, paraventricular thalamic nucleus, lateral habenular nucleus, hippocampus, amygdala, accumbens nucleus, piriform cortex, angular insular cortex, anterior olfactory nucleus, lateral septal nucleus, bed nucleus of stria terminalis, cingulate and medial prefrontal cortex, olfactory tubercle, medial and lateral orbital cortex. Nicotine, acting through central and peripheral nAChRs, produced c-Fos IR in areas that overlapped NIC-PM induced c-Fos expressing sites. These neuroanatomical data are the first to demonstrate that the CNS structures which are the direct targets of nicotine are also anatomical substrates for the peripheral sensory impact of nicotine.

No abstract

Considering the complexity and sometimes unpredictability of human behavior, and its role on workplace injuries, it is surprising that psychological research has contributed relatively little in studying workplace safety compared to technology development. The focus of this paper is thus shifted to psychological investigation of human factors to understand how a hazard is perceived, how a seen hazard is recognized, and how a decision must be made to avoid the hazard. The study is conducted through a three-step experiment including a questionnaire, Balloon-Analogue-Risk-Task (BART) test and safety photos and video clips with an eye tracking system to measure and analyze the relationship between the worker's personality and perceptions of risks and hazards. A very close relationship was found between the length of time a participant spent looking at a hazard and the number of hazards identified correctly.Participants with a higher safety score were able to recognize more safety hazards than those with lower safety score. Moreover, it was found that risk-takers were underestimating a hazard, whereas more cautious and less risky participants were overestimating a hazard.

Context. Cannabis is a herbaceous annual plant that belongs to the Cannabaceae family, which is used in the production of fibre, paper, oil and pharmaceutical products.Aims. The aim of this study was to identify drought-tolerant ecotypes and medicinal and industrial populations.Methods. Due to the medicinal and industrial importance of cannabis, 12 cannabis ecotypes were collected from different regions of Iran. Then, their agronomic and phytochemical characteristics were evaluated under different soil moisture conditions.Key results. The soil moisture levels had significant effects (P < 0.01) on the studied traits except for the 1000 seed weight. Based on duration of the growth period, the Tabas and Dasht-e-Moghan ecotypes were identified as early and late maturing ecotypes, respectively. Also, the highest stem dry weight and stem height/diameter ratio and the lowest seed yield were related to the Dasht-e-Moghan ecotype, which is valuable for fibre production. Based on seed yield (relative reduction) and some tolerance indexes, the Tabas and Tabrize ecotypes were shown to be the most tolerant and sensitive ecotypes, respectively. As soil moisture decreased, tetrahydrocannabinol levels increased and cannabidiol levels decreased. The highest amount of tetrahydrocannabinol was related to the Qom ecotype at 50% soil moisture and the highest amount of cannabidiol was related to the Rasht ecotype at 100% soil moisture.Conclusions. Generally, these ecotypes had different responses to soil moisture. Some ecotypes were valuable in terms of the production of pharmaceutical metabolites and some in terms of fibre production.Implications. Tolerant and sensitive ecotypes might be considered in production and also breeding programs.

Technology-driven and digital modeling developments are undeniably changing design and construction professions across the architecture, engineering and construction (AEC) industry. Today, it is impossible to conceive of an architectural practice without using computer tools right from initial design conceptualization to the creation of construction drawings for a given project. The computer applications delivered new capabilities to automate the design process and offered various possibilities of assistant to the designer. However, they are not smart enough yet to understand clients or end users' (e.g. building occupants) needs or expectations and innovate a building design. Artificial Intelligence (AI) could be a solution to making architectural and building design process less time-demanding and more organized with minimal manual interference. The ultimate aim of this study is to make AI part of AEC's digital journey, by making an intelligent agent able to both understand client needs and produce building designs. An intelligent design agent is defined as an autonomous entity that perceives client needs and makes design decisions towards achieving client satisfaction while balancing design and technical objectives. There are two challenges with respect to creating intelligent design agents; (1) finding quantitative ways of measuring client's needs and preferences, and (2) providing a mathematical framework for making design decisions. This study focuses primarily on the first challenge and proposes a solution for creating measurable data about the client's needs and preferences. First, an overview of technologies that support quantitative measurement of people's physical, emotional, and behavioral characteristics is presented. These technologies include eye tracking, facial expressions, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and galvanic skin response (GSR). This overview will enable us to understand potential applications of psychological measurement in the AI domain. This will be followed by a hypothetical design experiment to test the hypothesis that we can determine a subject's degree of satisfaction by recording and analyzing his or her eye-movements and facial expressions when presented with a collection of visual data, like window design options on a screen. The participants are provided with window design options and asked to evaluate each design based on their preference by giving it a score. While study subjects complete evaluation tasks for each design option, we record their interactions using eye-tracking and an automated facial expression tool. The objective is to find a relationship between subjects' preferences (as the independent variables) and the emotion expressed and the time spent on each design (as the dependent variables). This will allow us to provide a first look at how subjects interact with different attributes.

The risk of sand production must be evaluated from very early stages of field development planning to select the optimum well completion, and if required, identify the appropriate sand control options. However, during the appraisal or early development stages, sand production predictions often cannot be verified against field data because the lack of sanding observations. The analysis of predicting the onset and severity of sand production generally consists of using analytical or numerical rock mechanical models and ideally calibration with field sand production data. In this paper we show how in the absence of field sanding data, reliable sanding predictions can still be achieved by combining commonly used analytical and numerical prediction methods. This approach has been validated using a brown field dataset from South East Asia with several sand producing wells and multiple pressure depleted reservoirs. The analytical method uses a poro-elastic model and core-calibrated log-derived rock strength profiles with an empirical effective rock strength factor (ESF). The ESF should be calibrated against documented field sanding observations from wells with credible formation and drawdown pressures. The numerical method uses a poro-elasto-plastic model defined from triaxial core tests. The rock failure criterion in the numerical method is based on a critical strain limit (CSL) corresponding to the failure of the inner wall of thick-walled cylinder core tests or if existing field sanding data. In the brown oil field example, both analytical and numerical methods accurately predict the onset of sanding in the sand-prone wells. The core-based CSL defined by the numerical method match very well with the field sanding data with a very small margin of error. In contrast, the default and non-calibrated ESF values commonly used in the analytical method required considerable tuning to match with the field sanding data. This suggests that the core-based CSL can be used with a reasonable confidence for sand production prediction purposes in the field and the prediction can be as reliable as field-calibrated models. After the field validation, the predictions for planned infill wells by both analytical and numerical methods were consistent and similar. The paper will show how this approach is used in two other fields from the Asia Pacific region both at appraisal stages and with no field sanding data. Both cases show that different ESF values are required to match with the numerical simulations. In the absence of field sanding observation, particularly in the early stages of field development, the use of non-calibrated default ESF values in the analytical method could lead to erroneous sanding assessment and poor sand management decisions. Application of a combined analytical and numerical sanding evaluation enhances the reliability of sand production predictions. After a calibration is obtained, either with field sanding data or calibration against numerical simulation and core-based CSL criteria, the analytical method can be used with confidence and has the added benefits of simplicity and quick realizations of various scenarios and output plotting capabilities and inputs for sand control and well completion decisions.

No abstract

Accurate knowledge of in-situ stresses and rock mechanical properties are required for a reliable sanding risk evaluation. This paper shows an example, from the Waitsia Gas Field in the northern Perth Basin, where a robust well centric geomechanical model is calibrated with field data and laboratory rock mechanical tests. The analysis revealed subtle variations from the regional stress regime for the target reservoir with significant implications for sanding tendency and sand management strategies. An initial evaluation using a non-calibrated stress model indicated low sanding risks under both initial and depleted pressure conditions. However, the revised sanding evaluation calibrated with well test observations indicated considerable sanding risk after 500 psi of pressure depletion. The sanding rate is expected to increase with further depletion, requiring well intervention for existing producers and active sand control for newly drilled wells that are cased and perforated. This analysis indicated negligible field life sanding risk for vertical and low-angle wells if completed open hole. The results are used for sand management in existing wells and completion decisions for future wells. A combination of passive surface handling and downhole sand control methods are considered on a well-by-well basis. Existing producers are currently monitored for sand production using acoustic detectors. For full field development, sand catchers will also be installed as required to ensure sand production is quantified and managed.